Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries.

BACKGROUND:Despite being the most widely distributed mosquito-borne viral infection, estimates of dengue transmission intensity and associated burden remain ambiguous. With advances in the development of novel control measures, obtaining robust estimates of average dengue transmission intensity is k...

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Main Authors: Natsuko Imai, Ilaria Dorigatti, Simon Cauchemez, Neil M Ferguson
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2016-07-01
Series:PLoS Neglected Tropical Diseases
Online Access:http://europepmc.org/articles/PMC4939939?pdf=render
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spelling doaj-7e9190157e844d0fb5a148e4e7da964e2020-11-25T02:42:36ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352016-07-01107e000483310.1371/journal.pntd.0004833Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries.Natsuko ImaiIlaria DorigattiSimon CauchemezNeil M FergusonBACKGROUND:Despite being the most widely distributed mosquito-borne viral infection, estimates of dengue transmission intensity and associated burden remain ambiguous. With advances in the development of novel control measures, obtaining robust estimates of average dengue transmission intensity is key for assessing the burden of disease and the likely impact of interventions. METHODOLOGY/PRINCIPLE FINDINGS:We estimated the force of infection (λ) and corresponding basic reproduction numbers (R0) by fitting catalytic models to age-stratified incidence data identified from the literature. We compared estimates derived from incidence and seroprevalence data and assessed the level of under-reporting of dengue disease. In addition, we estimated the relative contribution of primary to quaternary infections to the observed burden of dengue disease incidence. The majority of R0 estimates ranged from one to five and the force of infection estimates from incidence data were consistent with those previously estimated from seroprevalence data. The baseline reporting rate (or the probability of detecting a secondary infection) was generally low (<25%) and varied within and between countries. CONCLUSIONS/SIGNIFICANCE:As expected, estimates varied widely across and within countries, highlighting the spatio-temporally heterogeneous nature of dengue transmission. Although seroprevalence data provide the maximum information, the incidence models presented in this paper provide a method for estimating dengue transmission intensity from age-stratified incidence data, which will be an important consideration in areas where seroprevalence data are not available.http://europepmc.org/articles/PMC4939939?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Natsuko Imai
Ilaria Dorigatti
Simon Cauchemez
Neil M Ferguson
spellingShingle Natsuko Imai
Ilaria Dorigatti
Simon Cauchemez
Neil M Ferguson
Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries.
PLoS Neglected Tropical Diseases
author_facet Natsuko Imai
Ilaria Dorigatti
Simon Cauchemez
Neil M Ferguson
author_sort Natsuko Imai
title Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries.
title_short Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries.
title_full Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries.
title_fullStr Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries.
title_full_unstemmed Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries.
title_sort estimating dengue transmission intensity from case-notification data from multiple countries.
publisher Public Library of Science (PLoS)
series PLoS Neglected Tropical Diseases
issn 1935-2727
1935-2735
publishDate 2016-07-01
description BACKGROUND:Despite being the most widely distributed mosquito-borne viral infection, estimates of dengue transmission intensity and associated burden remain ambiguous. With advances in the development of novel control measures, obtaining robust estimates of average dengue transmission intensity is key for assessing the burden of disease and the likely impact of interventions. METHODOLOGY/PRINCIPLE FINDINGS:We estimated the force of infection (λ) and corresponding basic reproduction numbers (R0) by fitting catalytic models to age-stratified incidence data identified from the literature. We compared estimates derived from incidence and seroprevalence data and assessed the level of under-reporting of dengue disease. In addition, we estimated the relative contribution of primary to quaternary infections to the observed burden of dengue disease incidence. The majority of R0 estimates ranged from one to five and the force of infection estimates from incidence data were consistent with those previously estimated from seroprevalence data. The baseline reporting rate (or the probability of detecting a secondary infection) was generally low (<25%) and varied within and between countries. CONCLUSIONS/SIGNIFICANCE:As expected, estimates varied widely across and within countries, highlighting the spatio-temporally heterogeneous nature of dengue transmission. Although seroprevalence data provide the maximum information, the incidence models presented in this paper provide a method for estimating dengue transmission intensity from age-stratified incidence data, which will be an important consideration in areas where seroprevalence data are not available.
url http://europepmc.org/articles/PMC4939939?pdf=render
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AT simoncauchemez estimatingdenguetransmissionintensityfromcasenotificationdatafrommultiplecountries
AT neilmferguson estimatingdenguetransmissionintensityfromcasenotificationdatafrommultiplecountries
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